Person:

Khanna, Arjun

From sequences of speech sounds, or letters, humans can extract rich and nuanced meaning through language. This capacity is essential for human communication. Yet, despite a growing understanding of the brain areas that support linguistic and semantic processing, the derivation of linguistic meaning in neural tissue at the cellular level and over the timescale of action potentials remains largely unknown. Here, we recorded from single cells in the left language-dominant prefrontal cortex as participants listened to semantically diverse sentences and naturalistic stories. By tracking their activities during natural speech processing, we discover a remarkably fine scale representation of semantic information by individual neurons. These neurons responded selectively to specific word meanings and reliably distinguished words from nonwords. Their activities were also dynamic, reflecting the words’ meanings based on their specific sentence contexts and independent of their phonetic form. Modeled collectively, we show how these cell ensembles accurately predicted the broad semantic categories of the words as they were heard in real-time during speech. We also show how they encoded the hierarchical structure of these meaning representations and how they mapped onto the population’s response patterns. Together, these findings reveal a detailed organization of semantic representations by prefrontal neurons in humans and begin to illuminate the cellular-level processing of meaning during language comprehension.